I help regulated enterprises deploy generative AI without creating unacceptable compliance risk. Where legal depth, communications strategy and AI governance converge.
Independent consultant working at the intersection of three disciplines most enterprises keep siloed — to their cost.
Most technologists lack regulatory depth. Most lawyers lack implementation experience. Most communications professionals lack both. I operate at the intersection of all three.
My work helps enterprises navigate the governance and workflow challenges of AI adoption in regulated environments — not just the technical ones. Because the technical challenges are solvable. It's the governance and workflow challenges that require deep domain understanding.
I built a proof-of-concept compliance AI from the ground up — not just conceptually, but through hands-on implementation — to validate my thinking and demonstrate exactly what governance-first AI deployment looks like in practice.
Work With MeSix capability areas, one integrated perspective — for enterprises deploying AI in regulated environments.
Design layered governance architecture for your regulatory environment. From input control to audit trail — a decision-first approach that constrains AI behaviour within policy boundaries.
Build RAG-based compliance systems for regulated communications. Production-ready systems integrated with existing legal and marketing workflows, not standalone tools teams ignore.
Ongoing compliance guidance for AI-generated content. Monthly advisory hours, regulatory monitoring, risk assessment for new use cases, and quarterly governance reviews.
Identify failure modes before deployment. Evaluate existing AI systems for compliance gaps, bias vectors, and audit-readiness against sector-specific regulatory requirements.
Shift-left compliance redesign — moving validation earlier in the workflow so legal teams focus on high-risk decisions, not routine screening. AI adoption is change management, not just tech.
Translate AI governance concepts into language that leadership and legal teams understand. Practical workshops on MITRE ATLAS frameworks, enterprise AI risk, and policy enforcement design.
Regulated enterprises adopting generative AI face a paradox — the technology that promises to accelerate communications workflows introduces compliance risk that slows everything down.
"Most enterprise AI deployments rely on Retrieval-Augmented Generation systems that do not enforce compliance constraints. They assume generation is always permissible. This leads to a critical failure mode: AI systems produce fluent, well-grounded, but potentially non-compliant communications."
Compliance is not a retrieval problem.
It is a decision governance problem.
Traditional systems optimize what the AI says. This system prioritizes whether the AI should say it at all — a decision-first architecture where generation is conditional and governed.
Input → Interpretation → Grounding → Decision → Generation → Evaluation → Audit
Intent classification, jurisdictional scoping, prohibited intent detection, and prompt injection resistance. Prevents incorrect routing and blocks unsafe inputs at source.
Structured ingestion of regulatory documents, metadata-driven indexing by jurisdiction and authority level. Reduces cross-jurisdictional or low-authority contamination.
The core control layer. Policy-constrained generation rules, hard rejection of non-compliant concepts, deterministic boundaries over probabilistic generation.
Violation detection, warning generation, severity classification (low / medium / high risk). Transforms outputs into risk-aware artefacts for informed decision-making.
Audit logging of queries, outputs and decisions. Tracks refusal rates, risk distribution, source usage analytics, and confidence scoring for regulatory defensibility.
Even with perfect retrieval, the LLM generated non-compliant ideas because it had no enforcement layer. Compliance requires decision governance, not just better RAG.
A beautifully written but non-compliant response is worse than a rough but safe one. Severity classification and hard rejection are more important than fluency.
The AI doesn't exist in isolation — it needs to fit into existing legal/marketing workflows. Value comes from how it changes the workflow, not the AI's accuracy alone.
"Usually compliant" isn't good enough. Hard boundaries beat soft guidance. Pattern-based rejection fires before LLM generation even starts.
The governance framework is domain-agnostic. Swap the policy layer, retain the architecture.
Financial claims control, disclosure enforcement, prevention of misleading communications
Off-label messaging prevention, clinical claims validation, patient-facing communication control
ASCI guidelines, surrogate advertising rules, jurisdiction-specific content constraints
Regulatory communications, safety claims, compliance-driven product messaging
Patient data governance, clinical communication standards, regulatory submission support
Structured engagements designed around your stage of AI adoption.
For enterprises planning AI deployment across regulated functions
For enterprises with a defined use case and deployment readiness
For enterprises with deployed AI systems needing continuous oversight
If you're navigating AI adoption in a regulated environment and this resonates, I'd like to hear about your situation. No obligation — just a conversation.
Thank you for reaching out. I'll get back to you within 48 hours.